AI Isn’t Replacing Junior Devs - It’s Exposing Their True Value

Why the smartest AI-first companies are still hiring at least some junior humans.

I'm excited to introduce Artur Grigio as the guest author of this article. Artur is a Principal Engineer at Common Sense, where he's building AI tooling to help local governments and municipalities make an impact. Artur and I work together daily navigating the realities of AI-first development—managing coding agents, vibe coding, and figuring out what actually works. With nearly a decade of experience across AWS, Amazon, and high-growth startups, Artur has built everything from fraud detection systems to horizontally scalable cloud infrastructure. He's seen firsthand how AI changes the developer workflow—and why human judgment still matters. Connect with Artur on LinkedIn to follow his work at the intersection of AI and public sector impact.

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I recently joined Adam at Common Sense, a VC-backed AI company with an AI-first mentality. In other words, I am a manager of AI coding agents, and I vibe code a lot.

Every day, I go back and forth between having my mind blown by AI to wondering how it can be so thoughtless. Particularly as we hire and scale, I’ve been wondering if Zuck was right when he told Rogan “all junior and mid-level developer jobs will be gone by the end of 2025.”

It’s already 2026 and yet the anxiety behind that statement hasn’t disappeared. Models write code faster than humans, reason across your entire repository, and never get tired. Are the CEOs right?

What I’ve seen so far is not the end of junior developers—but a misunderstanding of their value. As public company CEOs increasingly point to productivity gains from GenAI as the justifications for large layoffs, we’d recommend considering these three points before you fire or stop hiring all your junior developers.

1. Cost: Humans Are Still Cheaper Than You Think

A single serious instruction to a frontier model can easily consume 30K–60K tokens. AI is incentivized to be verbose. Larger contexts, richer reasoning, more retries, more agents—token usage explodes fast.

Check out Claude’s initial set of instructions. If you think you’re saving tokens by not saying “thank you” or “please”… those are drops in the bucket.

There are two popular schools of thought about where this goes next:

• Optimistic view: Token costs will approach $0 as compute becomes abundant and we move from training-heavy to inference-heavy usage.

• Monetization view: AI companies will simply allow larger and larger contexts so they can charge for richer inputs—codebases, documents, images, video, everything.

But the reality is much simpler.

OpenAI, Anthropic, and Google are locked in a race. Models are released in near lockstep. Each generation requires enormous capital expenditure—compute, energy, talent, and training time. And yet the lifecycle of each model is incredibly short. Many never come close to recouping their costs. Meanwhile, they are each pouring billions into costs that consumers don’t really see, so while it’s hard to know for sure, their pricing could be a competitive mirage.

It wouldn’t be surprising to learn that today’s frontier models are 90–95% subsidized for end users. Further, a smart pricing strategy would be to wait years for lock-in and then dramatically raise prices because the switching costs will be too high to do anything other than pay.

Eventually, that bill will come due.

When it does, token pricing won’t be $0.15 per million tokens for the best models. It could be $15+ per million tokens—or more—if you want state-of-the-art performance, reliability, and privacy guarantees. Let’s be honest, how many of us still use GPT-4.0? It’s not even a dropdown in Cursor!.

At that point, a junior developer who can reason, refactor, and maintain systems looks less like a cost—and more like a bargain. Counterintuitively, the junior developer might become the more efficient spend, or at least not the “no brainer” to outsource to AI as it is portrayed today.

2. Someone Has to Wrangle the Codebase

AI generates code aggressively.

That’s not a bug—it’s the business model. More tokens means more value captured for foundational model companies. But code generation at scale creates a new problem: entropy.

AI doesn’t feel architectural pain. It doesn’t sense when abstractions are leaking, when naming conventions drift, or when a codebase becomes conceptually incoherent. It will happily generate five different patterns to solve the same problem across five files.

Left unchecked, you don’t get a clean system. ifs are nested in for loops, which are nested in while loops… AI doesn’t care that your brain can only go 4-6 levels deep.

Junior developers are uniquely valuable here. They:

• Are lazy! They want to reuse code so much that they eventually stumble onto a good, succinct, simple solution.

• Are afraid of big PRs and won’t submit Sonnet’s rewrites of the entire component, just because you asked it to useState.

• Normalize patterns, simplify logic, and reduce duplication

• Turn AI output into maintainable human systems

In other words, they act as entropy reducers. The AI lacks even basic mental awareness of why your business exists and what might come next. It knows how to write a PaymentProcessor function, but it doesn’t know that the company is planning to pivot to subscriptions next month. A human junior developer sits in the meetings, hears the “why” and builds for a future that isn’t in the current prompt context.

AI can write a thousand lines in a minute. But someone still has to review it and make the codebase understandable again. That work does not disappear—it increases.

3. When the Product Breaks, Humans Fix It

More generated code means more surface area.

More surface area means more edge cases.

More edge cases means your product will break—in new, creative, and highly inconvenient ways.

AI is excellent at proposing fixes in isolation. It is far worse at:

• Understanding real user behavior

• Debugging production-only failures

• Reasoning about partial outages, degraded states, or cascading failures

• Making judgment calls under uncertainty

• And, it won’t press “Acknowledge” on that 2AM emergency page, which will escalate to you

This is where junior developers grow into indispensable engineers.

They triage bugs. They reproduce issues. They follow logs, metrics, and traces. They learn how the system actually behaves, not how it was designed to behave. Over time, they become the people who know where the bodies are buried. Without them, who will write your Root Cause Analysis (correctly) instead of regurgitating the same exact failure states time after time?

A system maintained only by AI becomes fragile.

Of course, we assume humans remain the primary consumers of source code for some time to come. If the next generation of LLMs can navigate ‘messy’ logic as easily as clean abstractions, the premium on code elegance will vanish, giving way to ‘dark’ codebases—systems that are mathematically functional and perfectly optimized for machine execution, but fundamentally illegible to any human mind. However, we’ve seen very limited evidence that we’ve reached this reality. AI fixes a lot of small bugs, but has poor performance on medium or high complexity issues across the code base.

Our Recommendation

Hire SOME amazing, thoughtful junior developers that use their brains AND use AI.

What is changing is that AI is replacing the illusion that software development was ever just about typing syntactically correct code.

Junior developers are how you:

• Control costs in an increasingly expensive AI ecosystem

• Keep codebases coherent instead of chaotic, and protected from serious bugs

• Build the next generation of senior engineers who actually understand the system
  

If you stop hiring juniors, you don’t eliminate the work, you just defer the pain — and make it much more expensive later.